Place-LT.
Following previous works [1]}, [2]}, we compare to the Lifted loss [3]}, Focal loss [4]}, Range Loss [5]}, OLTR [2]}, tau-norm [1]} and LWS [1]}. Table REF  shows that the recall loss outperforms other loss including SDN-CE. The result is two-fold. First, the strong performance of the baseline, SDN-CE agrees with the finding in [1]} that imbalance affects the classifier more than the backbone and a simple decoupling trick can outperform carefully designed losses. Second, the result validates our claim that the recall loss is a more suitable feature loss for imbalanced datasets when comparing to SDN-CE. iNaturalist2018.
On iNaturalist we compare to LDAM-DRW [10]}, BBN [11]}, tau-norm [1]} and LWS [1]}. This dataset is the most challenging due to its extremely large number of classes (8,142). Table REF  shows the results of SDN-recall, SDN-CE, and all compared methods. SDN-recall outperforms all other methods including SDN-CE. It is worth mentioning that both LDAM-DRW and BBN proposed to finetune the feature extractor with a balanced loss using a two-stage and two-branch strategy respectively. Recall loss trains a feature backbone in an end-to-end manner and outperforms other techniques that require careful hyperparameter tuning.
